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Adaptive AI decision interface for autonomous electronic material discovery 自主电子材料发现的自适应人工智能决策接口
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00318-3
Yahao Dai, Henry Chan, Aikaterini Vriza, Jingyuan Fan, Fredrick Kim, Yunfei Wang, Wei Liu, Naisong Shan, Jing Xu, Max Weires, Yukun Wu, Zhiqiang Cao, C. Suzanne Miller, Ralu Divan, Xiaodan Gu, Chenhui Zhu, Sihong Wang, Jie Xu
Artificial intelligence (AI)-powered autonomous experimentation (AE) accelerates materials discovery, but its use for electronic materials is limited by data scarcity from lengthy and complex design–fabricate–test–analyze cycles. Unlike human scientists, even current advanced AI–AE systems lack the adaptability for informative, real-time decisions with limited datasets. Here we developed an AI decision interface featuring an AI advisor for real-time monitoring, analysis and interactive human–AI collaboration, enabling active adaptation to different experimental stages and types. We applied this platform to an important class of electronic materials, mixed ion–electron conducting polymers, to study multiscale morphology and properties. Using organic electrochemical transistors to evaluate the mixed-conducting figure of merit, defined as the product of charge-carrier mobility and volumetric capacitance (μC*), our platform achieved a broad μC* range from 166 to 1,275 F cm−1 V−1 s−1 in just 64 autonomous trials. The analysis identified two key structural factors for higher volumetric capacitance: larger crystalline lamellar spacing and higher specific surface area, and uncovered a previously unknown polymer polymorph. This study reports on an AI-powered autonomous experimentation platform that overcomes data scarcity in electronic materials discovery by using an AI advisor for real-time progress monitoring, data analysis and interactive human–AI collaboration. Applied to mixed ion–electron conducting polymers, it rapidly optimized performance in 64 experimental trials, revealing morphology–property relationships and an unreported polymer polymorph.
人工智能(AI)驱动的自主实验(AE)加速了材料的发现,但其在电子材料中的应用受到冗长而复杂的设计-制造-测试-分析周期的数据稀缺的限制。与人类科学家不同,即使是目前先进的人工智能- ae系统也缺乏对有限数据集的信息实时决策的适应性。在这里,我们开发了一个人工智能决策界面,其中包含一个人工智能顾问,用于实时监控、分析和人机交互协作,能够主动适应不同的实验阶段和类型。我们将该平台应用于一类重要的电子材料,混合离子-电子导电聚合物,以研究多尺度形态和性质。使用有机电化学晶体管来评估混合导电优值,定义为电荷载流子迁移率和体积电容(μC*)的乘积,我们的平台在64次自主试验中实现了从166到1275 F cm−1 V−1 s−1的宽μC*范围。分析确定了高容量电容的两个关键结构因素:更大的晶体片层间距和更高的比表面积,并揭示了以前未知的聚合物多晶型。本研究报告了一个人工智能驱动的自主实验平台,该平台通过使用人工智能顾问进行实时进度监控、数据分析和人机交互协作,克服了电子材料发现中的数据稀缺问题。应用于混合离子-电子导电聚合物,它在64个实验试验中快速优化了性能,揭示了形态-性能关系和未报道的聚合物多晶态。
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引用次数: 0
New shifts in olefin production 烯烃生产的新转变
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00330-7
Yanfei Zhu
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引用次数: 0
Casting pathways for net zero 为净零铸造路径
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00333-4
Mo Qiao
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引用次数: 0
Take it past the limit 让它超过极限
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00347-y
Mo Qiao
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引用次数: 0
Enzymes can multitask 酶可以一心多用
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00311-w
Alessio Lavino
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引用次数: 0
A century-old reaction finds its rhythm 一个世纪的反应找到了节奏
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00346-z
Thomas Dursch
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引用次数: 0
Wrapping up 2025 结束2025年
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00350-3
We take a look back at some standout topics, research and initiatives that shaped Nature Chemical Engineering’s 2025 content.
我们来回顾一下影响《自然化学工程2025》内容的一些突出的主题、研究和倡议。
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引用次数: 0
From solvation shells to power cells 从溶剂化壳到动力电池
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00304-9
Kara D. Fong
Kara Fong reflects on bridging the molecular and continuum scales of ion transport, showing how microscopic fluctuations shape device-scale performance in batteries.
Kara Fong反映了离子传输的分子和连续尺度的桥接,展示了微观波动如何影响电池的设备尺度性能。
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引用次数: 0
The next era of Fischer−Tropsch technology Fischer - Tropsch技术的下一个时代
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00348-x
July 2025 marked the centennial anniversary of the Fischer−Tropsch process. This Editorial celebrates this milestone and looks ahead to the next era of Fischer−Tropsch technological innovation.
2025年7月是费托法诞生100周年。这篇社论庆祝了这一里程碑,并展望了费托技术创新的下一个时代。
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引用次数: 0
An enzymatic match made in high dimension 高维度的酶匹配
Pub Date : 2025-12-18 DOI: 10.1038/s44286-025-00319-2
Thomas Dursch
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引用次数: 0
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